A comparison of iteratively reweighted least squares and kalman filter with em in measurement error covariance estimation

Yang, Y, Brown, T, Moran, W, Wang, X, Pan, Q and Qin, 2016, 'A comparison of iteratively reweighted least squares and kalman filter with em in measurement error covariance estimation', in Proceedings of the19th International Conference on Information Fusion (FUSION 2016), Heidelberg, Germany, 5-8 July 2016, pp. 1-6.


Document type: Conference Paper
Collection: Conference Papers

Title A comparison of iteratively reweighted least squares and kalman filter with em in measurement error covariance estimation
Author(s) Yang, Y
Brown, T
Moran, W
Wang, X
Pan, Q
Qin,
Year 2016
Conference name FUSION 2016
Conference location Heidelberg, Germany
Conference dates 5-8 July 2016
Proceedings title Proceedings of the19th International Conference on Information Fusion (FUSION 2016)
Publisher IEEE
Place of publication United States
Start page 1
End page 6
Total pages 6
Abstract An unknown measurement error covariance in a stochastic dynamical system is to be estimated from measurements. A least squares approach is implemented by extending the iteratively reweighted least squares (IRLS) technique to handle system dynamics over a time window. The performance of this method, in terms of convergence rate and error, is compared to the standard Kalman Filter Expectation-Maximization (KFEM) approach via simulations of a single moving target with known stochastic dynamics tracked by two sensor measurements. We demonstrate that the extended IRLS outperforms KFEM in estimation accuracy. It also has a slightly better convergence rate at most epochs under any of a more uncertain, less uncertain, or re-estimated prior for the KFEM method.
Subjects Applied Statistics
Stochastic Analysis and Modelling
Copyright notice © 2016 ISIF
ISBN 978099645274
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